Image processing systems use noise measurement algorithms to measure and filter out noise signals. Image processing techniques, therefore, are usually subject to the noise content of the source image. In order to reduce the effect of noise on the processing, a method for measuring the noise contained by the source is required. Once determined, the measured noise can be subtracted from the image.
Spatial noise is generally the dominant noise in image processing, while temporal noise generally plays a minor part. In particular, the contribution of the pixel structure to the noise power spectrum, which shows up as sharp spikes at spatial frequencies beyond the Nyquist frequency, is important. This type of noise image can make subtle structures invisible or add nonexistent patterns to the image.
Currently, various noise measurement methods are used. Most of these methods though, are either hardware inefficient, dependant on the spatial or temporal pixel motion of the image, or operate in the blanking interval, and are therefore independent of the pixel information. Although, those methods that operate in the blanking interval are robust, they are often unusable due to various signaling methods inserted in the blanking interval, (e.g., teletext, closed caption, etc.).
Therefore, there exists a need for an improved noise measurement method that overcomes the shortcomings of current methods.